Article2009

Locating Facilities in the Presence of Disruptions and Incomplete Information*

Oded Berman · Dmitry Krass · Mozart B.C. Menezes

69 citations0/0 files storedDecision Sciences

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Facility Location and Emergency Management Reliability and Maintenance Optimization Supply Chain and Inventory Management

Abstract

ABSTRACT In this article, we analyze a location model where facilities may be subject to disruptions. Customers do not have advance information about whether a given facility is operational or not, and thus may have to visit several facilities before finding an operational one. The objective is to locate a set of facilities to minimize the total expected cost of customer travel. We decompose the total cost into travel, reliability, and information components. This decomposition allows us to put a value on the advance information about the states of facilities and compare it to the reliability and travel cost components, which allows a decision maker to evaluate which part of the system would benefit the most from improvements. The structure of optimal solutions is analyzed, with two interesting effects identified: facility centralization and co‐location; both effects appear to be stronger than in the complete information case, where the status of each facility is known in advance.

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References

1964Optimum Locations of Switching Centers and the Absolute Centers and Medians of a Graph 2001Supply Chain Management under the Threat of International Terrorism 2005Reliability Models for Facility Location: The Expected Failure Cost Case 1971Optimal Center Location in Simple Networks 2007Facility Reliability Issues in Network p-Median Problems: Strategic Centralization and Co-Location Effects 2007Facility Reliability Issues in Network p-Median Problems: Strategic Centralization and Co-location Effects 1985Optimal Server Location on a Network Operating as an M/G/1 Queue 1987Heuristic Solution Methods for Two Location Problems with Unreliable Facilities

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2017A stochastic programming approach for Shelter location and evacuation planning 2013Probability chains: A general linearization technique for modeling reliability in facility location and related problems 2021Designing an optimal sustainable supply chain system considering pricing decisions and resilience factors 2013Solving the p-hub Median Problem Under Intentional Disruptions Using Simulated Annealing 2019A Benders Decomposition Method for Designing Reliable Supply Chain Networks Accounting for Multimitigation Strategies and Demand Losses

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